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Register a new userThe Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations
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Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted such as weekly, daily, or even many times a day. This high intensity of adaptation is facilitated by the ability of digital technology to continuously collect information about an individuals current context and deliver treatments adapted to this information. The micro-randomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs operate in, and take advantage of, the rapidly time-varying digital intervention environment. MRTs can be used to address research questions about whether and under what circumstances particular components of a JITAI are effective, with the ultimate objective of developing effective and efficient components. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to discuss the possibilities this emerging optimization trial design offers for future research in the behavioral sciences, education, and other fields. We briefly review key elements of JITAIs, and then describe three case studies of MRTs, each of which highlights research questions that can be addressed using the MRT and experimental design considerations that might arise. We also discuss a variety of considerations that go into planning and designing an MRT, using the case studies as examples.
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Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted--weekly, daily, or even many times a day. The micro-randomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.
Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing papers and making the final acceptance/rejection decisions. Given that human decision making is known to be susceptible to various cognitive biases, it is important to understand which (if any) biases are present in the peer-review process and design the pipeline such that the impact of these biases is minimized. In this work, we focus on the dynamics of between-reviewers discussions and investigate the presence of herding behaviour therein. In that, we aim to understand whether reviewers and more senior decision makers get disproportionately influenced by the first argument presented in the discussion when (in case of reviewers) they form an independent opinion about the paper before discussing it with others. Specifically, in conjunction with the review process of ICML 2020 -- a large, top tier machine learning conference -- we design and execute a randomized controlled trial with the goal of testing for the conditional causal effect of the discussion initiators opinion on the outcome of a paper.
Experimentation has become an increasingly prevalent tool for guiding decision-making and policy choices. A common hurdle in designing experiments is the lack of statistical power. In this paper, we study the optimal multi-period experimental design under the constraint that the treatment cannot be easily removed once implemented; for example, a government might implement a public health intervention in different geographies at different times, where the treatment cannot be easily removed due to practical constraints. The treatment design problem is to select which geographies (referred by units) to treat at which time, intending to test hypotheses about the effect of the treatment. When the potential outcome is a linear function of unit and time effects, and discrete observed/latent covariates, we provide an analytically feasible solution to the optimal treatment design problem where the variance of the treatment effect estimator is at most 1+O(1/N^2) times the variance using the optimal treatment design, where N is the number of units. This solution assigns units in a staggered treatment adoption pattern - if the treatment only affects one period, the optimal fraction of treated units in each period increases linearly in time; if the treatment affects multiple periods, the optimal fraction increases non-linearly in time, smaller at the beginning and larger at the end. In the general setting where outcomes depend on latent covariates, we show that historical data can be utilized in designing experiments. We propose a data-driven local search algorithm to assign units to treatment times. We demonstrate that our approach improves upon benchmark experimental designs via synthetic interventions on the influenza occurrence rate and synthetic experiments on interventions for in-home medical services and grocery expenditure.
Augmented Reality (AR) bridges the gap between the physical and virtual world. Through overlaying graphics on natural environments, users can immerse themselves in a tailored environment. This offers great benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. While a variety of apps currently exist, usability issues can discourage users from embracing AR. Interfaces can become cluttered with icons, with POI occlusion posing further challenges. In this paper, we use user-centred design (UCD) to develop an AR tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Whereas previous user-centred work has designed mock-ups, we iteratively develop a full Android app. This includes overhead maps and route navigation, in addition to a detailed AR browser. The final product is evaluated by 20 users, who participate in a tourism task in a UK city. Users regard the system as usable and intuitive, and suggest the addition of further customisation. We finish by critically analysing the challenges of a user-centred methodology.
Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that are tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial (SMART) is a research tool which allows for the construction of effective DTRs. We derive easy-to-use formulae for computing the total sample size for three common two-stage SMART designs in which the primary aim is to compare mean end-of-study outcomes for two embedded DTRs which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a SMART can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a SMART. The SMART design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for DTR effect analyses using a longitudinal outcome from a SMART, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a SMART aimed at developing a DTR for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.
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